Abstract
The classification of multivariate time series in terms of their corresponding temporal dependence patterns is a common problem in geosciences, particularly for large datasets resulting from environmental monitoring networks. Here a wavelet-based clustering approach is applied to sea level and atmospheric pressure time series at tide gauge locations in the Baltic Sea. The resulting dendrogram discriminates three spatially-coherent groups of stations separating the southernmost tide gauges, reflecting mainly high-frequency variability driven by zonal wind, from the middle-basin stations and the northernmost stations dominated by lower-frequency variability and the response to atmospheric pressure.
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Acknowledgments
Tide gauge data kindly provided by DMI (K. Madsen), SMHI (T. Hammarklint) and UHSLC. S.M. Barbosa acknowledges support of the FCT—Fundação para a Ciência e a Tecnologia (contract under programme IF2013 and project UID/EEA/50014/2013). This work was supported by the European Regional Development Fund (FEDER) through the COMPETE programme and by the Portuguese Government through the FCT, in the scope of the project UID/MAT/04106/2013 (Centro de I&D em Matemática e Aplicações, http://cidma.mat.ua.pt/) and projects PEst-OE/EEI/UI0127/2014 and UID/CEC/00127/2013 (Instituto de Engenharia Electrónica e Informática de Aveiro, IEETA/UA, http://www.ieeta.pt). S. Gouveia acknowledges the postdoctoral grant by FCT (ref. SFRH/BPD/87037/2012). A.M. Alonso acknowledges support of the Ministerio de Economía y Competitividad projects ECO2011-25706 and ECO2012-38442.
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Barbosa, S.M., Gouveia, S., Scotto, M.G. et al. Wavelet-Based Clustering of Sea Level Records. Math Geosci 48, 149–162 (2016). https://doi.org/10.1007/s11004-015-9623-9
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DOI: https://doi.org/10.1007/s11004-015-9623-9